Electroencephalography (EEG) was the first of the noninvasive brain measures in neuroscience. Technical advances over the last 100 years or so have rendered EEG a true brain imaging technique. Here, we provide an accessible primer on the biophysics of EEG, on measurement aspects, and on the analysis of EEG data. We use the example of event-related potentials (ERPs), although the issues apply equally to other varieties of EEG signals, and provide an overview of analytic methods at the base of the so-called electrical neuroimaging framework. We detail the interpretational strengths of electrical neuroimaging for organizational researchers and describe some domains of ongoing technical developments. We likewise emphasize practical considerations with the use of EEG in more real-world settings. This primer is intended to provide organizational researchers specifically, and novices more generally, an access point to understanding how EEG may be applied in their research.

In the organizational sciences, scholars are increasingly using experience sampling methods (ESM) to answer questions tied to intraindividual, dynamic phenomenon. However, employing this method to answer organizational research questions comes with a number of complex—and often difficult—decisions surrounding: (1) how the implementation of ESM can advance or elucidate prior between-person theorizing at the within-person level of analysis, (2) how scholars should effectively and efficiently assess within-person constructs, and (3) analytic concerns regarding the proper modeling of interdependent assessments and trends while controlling for potentially confounding factors. The current paper addresses these challenges via a panel of seven researchers who are familiar not only with implementing this methodology but also related theoretical and analytic challenges in this domain. The current paper provides timely, actionable insights aimed toward addressing several complex issues that scholars often face when implementing ESM in their research.

The time is ripe for a renewed and interdisciplinary approach to organizational research that incorporates neuroscientific techniques. Like all methods, they have methodological, analytical, and interpretational limitations; however, the potential gains from using these techniques are far more considerable. We have therefore assembled a succinct yet authoritative collection of articles on the topic of neuroscience in organizational research, to serve as a solid introduction to the methods of neuroscience and what they can accomplish. The special topic is organized into two parts. The first includes a set of accessible reviews of the palette of brain imaging, mapping, and stimulation techniques (fMRI, fNIRS, EEG, MEG, and NIBS) as well as examples of the application of neuroscience methods to various disciplines including economics, marketing, finance, organizational behavior, neuroethology, as well an integrative translational critique on a variety of applications. The second is a collection of articles resulting from a competitive call for submissions that cover various neuroscience topics but also address important methodological and philosophical issues. The articles lay out a roadmap for the effective integration of neuroscientific methods into organizational research.

Theories are the core of any science, but many imprecisely stated theories in organizational and management science are hampering progress in the field. Computational modeling of existing theories can help address the issue. Computational models are a type of formal theory that are represented mathematically or by other formal logic and can be simulated, allowing theorists to assess whether the theory can explain the phenomena intended as well as make testable predictions. As an example of the process, Locke’s integrated model of work motivation is translated into static and dynamic computational models. Simulations of these models are compared to the empirical data used to develop and test the theory. For the static model, the simulations revealed largely strong associations with robust empirical findings. However, adding dynamics created several challenges to key precepts of the theory. Moreover, the effort revealed where empirical work is needed to further refine or refute the theory. Discussion focuses on the value of computational modeling as a method for formally testing, pruning, and extending extant theories in the field.

Self-report personality scales are used frequently in personnel selection. Traditionally, researchers have assumed that individuals respond to items within these scales using a single-decision process. More recently, a flexible set of item response (IR) tree models have been developed that allow researchers to investigate multiple-decision processes. In the present research, we found that IR tree models fit the data better than a single-decision IR model when fitted to seven self-report personality scales used in a concurrent criterion-related validity study. In addition, we found evidence that the latent variable underlying the direction of a response (agree or disagree) decision process predicted job performance better than latent variables reflecting the other decision processes for the best fitting IR tree model.

Background and MotivationWith an ever-growing body of

This article presents a methodology that organizational scholars can use to analyze, explain, and critically interpret the role of visual rhetoric in organizational communications. Corporations invest heavily in the visual design of organizational communications, including corporate reports, recognizing the distinctive role and benefits of visual imagery, as well as the rhetorical function of these documents. Current analytical approaches to visual rhetoric are either predominantly theoretical (with little structured guidance) or procedural (with little acknowledgment of important epistemic questions). This leaves a need for a methodology that combines a clear theoretical framework with explicit guidance on how to analyze visual rhetoric. This article bridges this analytic gap by offering a methodology that integrates a theoretical foundation of a priori analytical categories informed by selected writings of the French cultural theorist Roland Barthes with three abductively derived phases (categorical analysis, content analysis, rhetorical analysis) for analysis of visual design of corporate reports. We apply this methodology in an examination of a Qantas Annual Review and articulate our contributions to the field of organizational research methods.

We present the SEMWISE (structural equation modeling for within-subject experiments) approach for analyzing policy capturing data. Policy capturing entails estimating the weights (or utilities) of experimentally manipulated attributes in predicting a response variable of interest (e.g., the effect of experimentally manipulated market-technology combination characteristics on perceived entrepreneurial opportunity). In the SEMWISE approach, a factor model is specified in which latent weight factors capture individually varying effects of experimentally manipulated attributes on the response variable. We describe the core SEMWISE model and propose several extensions (how to incorporate nonbinary attributes and interactions, model multiple indicators of the response variable, relate the latent weight factors to antecedents and/or consequences, and simultaneously investigate several populations of respondents). The primary advantage of the SEMWISE approach is that it facilitates the integration of individually varying policy capturing weights into a broader nomological network while accounting for measurement error. We illustrate the approach with two empirical examples, compare and contrast the SEMWISE approach with multilevel modeling (MLM), discuss how researchers can choose between SEMWISE and MLM, and provide implementation guidelines.

This article provides a review and synthesis of person-centered analytic (i.e., clustering) methods in organizational psychology with the aim of (a) placing them into an organizing framework to facilitate analysis and interpretation and (b) constructing a set of practical recommendations to guide future person-centered research. To do so, we first clarify the terminological and conceptual issues that still cloud person-centered approaches. Next, we organize the diverse kinds of person-centered analyses into two major statistical approaches, algorithmic and latent-variable approaches. We then present a literature review that quantifies how these two approaches have been used within our field, identifying trends over time and typical study characteristics. Out of this review, we construct a unifying taxonomy of the five ways in which clusters are differentiated: (1) construct-based patterns, (2) response-style patterns, (3) predictive relations, (4) growth trajectories, and (5) measurement models. We also provide a set of practical guidelines for researchers and highlight a few remaining questions and/or areas in which future work is needed for further advancing person-centered methodologies.